What Is Population And Sampling In Research Pdf

what is population and sampling in research pdf

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Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree.

It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association.

Research: Population and Sample

Published on September 19, by Shona McCombes. Revised on February 25, Instead, you select a sample. The sample is the group of individuals who will actually participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole.

There are two types of sampling methods:. You should clearly explain how you selected your sample in the methodology section of your paper or thesis. Table of contents Population vs sample Probability sampling methods Non-probability sampling methods Frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population can be defined in terms of geographical location, age, income, and many other characteristics. It can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or students in a single school. If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population and nobody who is not part of that population. You are doing research on working conditions at Company X. Your population is all employees of the company. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis.

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance. You want to select a simple random sample of employees of Company X. You assign a number to every employee in the company database from 1 to , and use a random number generator to select numbers.

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals. All employees of the company are listed in alphabetical order.

From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected 6, 16, 26, 36, and so on , and you end up with a sample of people. If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample.

For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees. Stratified sampling involves dividing the population into subpopulations that may differ in important ways.

It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample. To use this sampling method, you divide the population into subgroups called strata based on the relevant characteristic e. Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup.

Then you use random or systematic sampling to select a sample from each subgroup. The company has female employees and male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender.

Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of people. Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample.

Instead of sampling individuals from each subgroup, you randomly select entire subgroups. If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. The company has offices in 10 cities across the country all with roughly the same number of employees in similar roles. See an example. In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited.

If you use a non-probability sample, you should still aim to make it as representative of the population as possible. Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

A convenience sample simply includes the individuals who happen to be most accessible to the researcher. You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves e. Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others. You send out the survey to all students at your university and a lot of students decide to complete it.

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research. It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific.

An effective purposive sample must have clear criteria and rationale for inclusion. You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. You are researching experiences of homelessness in your city. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling. Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Scribbr is a website, not a journal. Hope that helps! Yes, you can use simple random sampling in a qualitative study.

However, this method is usually quite difficult and time-intensive to perform correctly unless your population is very small , and its advantages are mainly relevant to statistical analysis. That's why many researchers use non-probability methods to choose samples for qualitative studies. I personally found this information very helpful. But i would suggest you to include the alternative names of these topics too. So, i wanted to know if judgement and purposive sampling are the same?

Yes, judgement sampling is the same as purposive sampling. We'll update the article with a note on this :. Hi, Shona your article was so helpful l'm ecstatic now that i know all these sampling techniques. Hope you'll help. Thanks once again! Quota sampling is the non-probability equivalent of stratified sampling.

Instead of randomly selecting from strata that cover the whole population, researchers choose a "quota" of participants from different subgroups using a non-probability method. Yes, it's common for exploratory research to use non-probability sampling. This is because the aim of exploratory research is to explore a new problem or phenomenon and gain an initial understanding of it, not to make statistical inferences about a whole population.

This is a very smart and simple way of understanding all about sampling methods. Well done. My question however, is what type of sampling method is it when you decide to chose your sample on first come first served basis. Like using the first 50 subjects to arrive at the study area?

Methods of sampling from a population

The entire group of people or objects to which the researcher wishes to generalize the study findings Meet set of criteria of interest to researcher Examples. All institutionalized elderly with Alzheimer ' s in St. Samples Terminology used to describe samples and sampling methods. Could be extremely large if population is national or international in nature Frame is needed so that everyone in the population is identified so they will have an equal opportunity for selection as a subject element Examples. A list of all institutionalized elderly with Alzheimer ' s in St.

Home QuestionPro Products Audience. Sampling definition: Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and a cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in a research survey software for optimum derivation. Select your respondents.

Skip to main content. Lead Author s : Dr. Source: Edmodo. Student Price: Contact us to learn more. In this homework assignment students will be asked to understand population, sample and various sampling techniques. This content is licensed under the Creative Commons Attribution 4.

A population refers to any collection of specified group of human beings or of non-human entities such as objects, educational institutions, time units, geographical areas, prices of wheat or salaries drawn by individuals. a population with infinite number of members is known as infinite population.

Sampling In Research

Published on September 19, by Shona McCombes. Revised on February 25, Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

Population vs sample: what’s the difference?

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Statistics without tears: Populations and samples




Technology (URNCST) Journal. Keywords: protocol; proposal; study design; population; study setting; sampling; sample size; under.



This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with knowledge on the general issues on sampling that is the purpose of sampling in research, dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample size.

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which researcher sample is drawn in called the population. Since, researchers neither have time. nor the resources to analysis the entire.